Explaining Intraday Pattern of Trading Volume From the Order Flow Data

Explaining Intraday Pattern ofTrading Volume from the Order

Flow Data

Yi-Tsung Lee, Robert C.W. Fok and Yu-Jane Liu*

1. INTRODUCTION

Extensive studies have documented a pattern of usually largetrading volume at the market open, and in particular at the closein the New York Stock Exchange and Toronto Stock Exchange.For example, Wood, McInish and Ord (1985), McInish andWood (1990a), McInish and Wood (1992) and Lockwood andLinn (1990) found U-shaped patterns for intraday returns andtrading volume. Similar patterns have also been explored in someAsian stock markets. For instance, Chow, Lee, Liu and Liu(1994), Ho and Cheung (1991), as well as Ho, Cheung andCheung (1993) found extremely large trading volume at theclose in the Taiwan and Hong Kong stock markets. Hence, largetrading volume around market open and close is a globalphenomenon.

Many researchers dedicate their efforts to explain why suchpatterns exist. McInish and Wood (1990b), Harris (1989) andPorter (1992) suggested that day-end effects might account forthe pattern. Since different markets show similar intradaypatterns of trading volume, trading mechanisms may not be

Journal of Business Finance & Accounting, 28(1) & (2), January/March 2001, 0306-686X

ß Blackwell Publishers Ltd. 2001, 108 Cowley Road, Oxford OX4 1JF, UKand 350 Main Street, Malden, MA 02148, USA. 199

* The authors are respectively from the National Chung Cheng University, Taiwan;Shippensburg University, USA; and the National Chengchi University, Taiwan. Yi-TsungLee would like to acknowledge the financial support of the National Science Council forresearch presented in this article from grant No. NSC 88-2416-H-194-002-88-053. (Paperreceived August 1998, revised and accepted February 2000)

Address for correspondence: Yi-Tsung Lee, Department of Accounting, National ChungCheng University, 160 San-Hsing, Ming-Hsiung, Chia-Yi 62117, Taiwan, ROC.e-mail: [email protected]

responsible for the patterns. Information asymmetry has recentlybeen proposed as one of the possible explanations for thepattern. Admati and Pfleiderer (1988 and 1989) pioneered toconstruct a model and demonstrated that liquidity traders tendto trade together to reduce the monopoly power of insiders. Theclustering of uninformed traders draws informed traders to themarket because informed traders benefit more from their privateinformation when noise traders trade. Using an information-based model, Foster and Viswanathan (1990) contended thatinformation is accumulated during non-trading periods.Therefore, informed traders may wish to enter the market assoon as possible; otherwise, their private information will begradually revealed as transactions take place.

Brock and Kleidon (1992) proposed the risk-sharingmotivation. They suggested that day traders tend to shift therisk of holding positions overnight to other traders. Following theinsight of Brock and Kleidon (1992), Gerety and Mulherin(1992) asserted that traders who perform arbitrage functionsduring active trading do not want to retain their holdingsovernight. Their results indicate that closing volume is related tothe expected overnight volatility underscoring risk-sharingmotives. Additionally, the expected and unexpected volatilitywill affect the next open volume, which supports both the risk-sharing motives and information asymmetry hypothesis. Using amathematical model, Slezak(1994) showed that closures delaythe resolution of uncertainty, and thus redistribute risk acrosstime and traders. As a consequence, the redistribution alters riskpremium, liquidity costs, and the degree of informationasymmetry.

All of these studies, except Gerety and Mulherin (1992), aretheoretical researches. Gerety and Mulherin (1992) adoptedSchwert's model to estimate the expected and unexpectedvolatility. They validate the information asymmetry and risk-sharing hypothesis in explaining trading volume. However, theydid not address how informed and uninformed traders behaveduring the intraday periods. Studies on intraday trading yieldimportant policy implication. For example, Gerety and Mulherin(1992) drew inference on the effect of trading halt from thebehavior of trading volume around market close. AsBessembinder, Chan and Seguin (1996) claimed, `Despite the

200 LEE, FOK AND LIU

ß Blackwell Publishers Ltd 2001

importance of the topic, surprisingly little empirical research hasaddressed the determinant of trading volume.' To date, there isno close-up study on the trading behavior of different types ofinvestors and its impact on the intraday trading volume pattern.

This study extends the literature by examining the relationshipbetween investors' trading behaviors and trading volume duringintraday periods. The pivotal contribution of this study is to trackthe intraday trading behavior of informed and uninformedinvestors directly using a complete limit order book data of theTaiwan Stock Exchange. We examine the intraday pattern ofinformation orders and liquidity orders as well as the orderingstrategies of both informed and uninformed (liquidity) traders.

The study finds the following important pattern of intradaytrading: First both informed and uninformed investors tend toplace more orders at both the market open and the close.Second, real orders exhibit a J-shaped pattern while waitingorders are in a reversed J-shaped pattern. Third, the impact ofliquidity trading on volume is relatively larger than that of theinformation trading.

In this study, we use order flow data from the Taiwan stockmarket (TWSE). The data allows us to examine investors' tradingbehaviors directly. There are several merits of using the orderflow data: (1) We can exclude the impact of trading rules ofexecution; (2) TWSE is an agent market. Using the data from themarket excludes the influences of dealer or specialist systems inthe investigation of intraday patterns of trading volume; (3)Previous studies have used location in spreads to proxy forrelative pressure of buy and sell orders. As pointed out by Leeand Ready (1991), these measurements may be biased. Withorder flow data, we can identify directly whether a trade is buyer-initiated or seller-initiated; (4) It allows us to construct proxiesfor information trading and liquidity trading.

The following section investigates the intraday pattern oftrading volume in the Taiwan stock market based on the intradaytransaction data from March 1 to May 31, 1995. Testablehypotheses are constructed and variables used in the regressionanalysis are defined in Section 3. Empirical results are providedin Section 4. Finally, concluding remarks are made in Section 5.

INTRADAY PATTERN OF TRADING VOLUME 201


2. INTRADAY PATTERN OF TRADING VOLUME

(i) Data Descriptions

The Taiwan stock market uses a call system except for the open. Forthe open trade, orders with the same price are matched randomly.For other time intervals, orders are matched based on price-timepriority. The market opens a call at 9:00 A.M. by accumulating theentering orders from 8:30 A.M. to 9:00 A.M. The calls during theremaining periods (from 9:00 to 12:00, excluding the open trade)are executed for one minute on average (for more details, see Chow,Hsiao and Liu, 1999). It is an agency market in which no dealers orspecialists are involved in the market. Thus, using the data from theTaiwan stock market enables us to investigate intraday patterns in away that results are not contaminated by different auctionmechanisms in various intraday trading periods. Furthermore, sincemost stocks in the Taiwan stock market are actively traded, ourresults are not likely affected by nonsynchronous trading.

Order flow data and transaction data from the Taiwan stockmarket under study is for the period from March 1 to May 31, 1995.We have an electronic complete limit order book which providesdata on all trades including quotations, buy or sell-initiated sharesin lots and time-stamped. The data allows us to identify differenttypes of investors and their trading behaviors. In addition, the dataavoids the bias that may be caused by only investigating part of theorder flow files (e.g. Biais, Hillion and Spatt, 1995).

In order to distinguish traders' real trading intention versusdesire for information, data from individual stocks instead of themarket indices are examined. We analyze the 30 most activelytraded stocks in the sample period. The 30 stocks account formore than 46% of the total market value of the stocks traded inthe TWSE, therefore, the sample is representative.

(ii) Intraday Pattern of Trading Volume

The intraday pattern of trading volume for our sample firmsacross 31 time intervals is summarized in Figure 1. The first pointrepresents the open trade. The others are six-minute intervals.Previous studies find a U-shaped pattern for trading volume.Figure 1 indicates a different pattern for our sample firms.



Surprisingly, a J-shaped rather than a U-shaped pattern is found.The lowest trading volume occurs at the open trade. This couldnot be due to late reporting because the calls in the TWSE areexecuted no more than 90 seconds on average. The tradingshares jump up at 9:06, taper through the interior periodsgradually, and rise rapidly at the end of the trading day, especiallyfor the last six minutes. F test results indicate that trading volumeat the market close is statistically different from that of the opentrade and from those in the interior periods (9:06-11:54): F-open,

close and F-close, inn are 20.2 and 17.54 respectively, where F standsfor F-statistic, òpen' represents the open trade, ìnn' representsthe interior periods from 9:06 to 11:54, and `close' represents thelast trade interval (11:54±12:00). However, trading volume at theopen is not significantly different from those of the other timeintervals excepting the last trading interval (11:54±12:00).

The J-shaped pattern does not necessarily contradict to thefindings reported in previous studies. As Foster and Viswanathan(1990) reported, less active firms show a more pronounced U-

Figure 1

Intraday Volume



shaped pattern of trading volume. Our sample includes the mostactive stocks in the Taiwan market, so it is not surprising to find aless pronounced U-shaped pattern. Moreover, if the open trade isincluded into the 9:00±9:06 interval, trading volume confersmore closely to a U-shaped pattern. Nevertheless, Figure 1 showsthat trading volume is extremely large at the market close, i.e., aclosure effect is evident.

3. TESTING HYPOTHESES AND MEASUREMENT OF VARIABLES

(i) Testable Hypothesis

In the following, we investigate how trading volume is related withthe trading behaviors of informed and uninformed traders. Firstly,we examine if concentrated trading exists during the intradayperiod. Secondly, we investigate whether informed traders anduninformed traders cluster their orders at the market open andthe close. Finally, we examine the ordering strategy of informedand uninformed traders by decomposing total orders into real andwaiting orders. The testing hypotheses are listed below.

H1: Investors tend to place more orders at the open and theclose than at the interior periods.

Admati and Pfleiderer (1988 and 1989) showed mathematicallythat concentrated trading exists at the market open and theclose. They demonstrated that liquidity traders tend to tradetogether to reduce the monopoly power of insiders. Theclustering of uninformed traders draws informed traders to themarket. However, trading volume may not be a good proxy fortrading intention of investors, since trading volume may also beaffected by trading rules of execution. In particular, if the tradingrules for the open, close and the rest of the trading periods aredifferent, results based on trading volume may be biased.

To examine if large trading volume implies concentratedtrading, this study adopts original entering orders to examine thetraders` desires to place their orders. We hypothesize thatinvestors tend to place more orders at the market open andthe close than at the interior periods. Therefore, clusteringorders are expected around the market open and the close.



H2: The clustering of informed and uninformed traders atmarket open and the close contribute to the intradaypattern.

Admati and Pfleiderer (1988 and 1989) demonstrated thatliquidity traders and informed traders tend to cluster their tradeat the open and close. Foster and Viswanathan (1990) contendedthat informed traders might wish to enter the market at the opento avoid revealing their private information. In order to examinethese arguments, we classify total orders into informed anduninformed orders (or liquidity orders). We hypothesize thatinformed orders and uninformed orders at the open and theclose are larger than those at the rest of the trading intervals.Furthermore, concentrated trading by informed and uninformedtraders accounts for the intraday pattern of trading volume.

H3: Traders place orders strategically and conservatively at themarket open.

Slezak(1994) proved that closures delay the resolution ofuncertainty, thereby redistributing risk across time and traders.We hypothesize that traders strategically place their orders due toclosure effects. Due to high uncertainty generated from non-trading periods, traders place their orders conservatively at themarket open.

(ii) Measurement of Variables

To test the aforementioned hypotheses, we need to measureinvestor's trading desire and identify whether an investor is aninformed or uninformed trader. Measurements of the keyvariables used in this study are defined in the following section:

(a) Traders Desires

The indicators listed below are used to measure trading desires ofinvestors. Bi;t �Si;t� represents total buy (sell) orders at interval ion day t. Orders are expressed in terms of trading lots (LOT) andnumber of orders (NUM). The measurement interval, i, is sixminutes. There is always a trade-off between price priority andwaiting costs for traders to place their orders. If traders place alow (high) price to buy (sell) stocks, they prefer to wait for a good



opportunity to get better prices. Such orders may be invalid forexecution and reflect desires for price priority rather than realtrading intention. On the contrary, if traders place a high (low)price to buy (sell) stocks, they show great intention to have theirorders being executed. Such orders represent real tradingintention rather than desires for price priority. Therefore, weclassify total orders into two categories. Real buy (sell) orders atinterval i on day t, RBi;t �RSi;t� are buy (sell) orders that aregreater (lower) than or equal to two ticks from the previoustransaction prices. Waiting buy (sell) orders at interval i on day t,UBi,t (USi,t), are orders that are lower (greater) than or equal totwo ticks from the previous transaction prices. If investors havestrong desires to place their orders at market open and close, wewould find U-shaped patterns for real buy and sell orders.

(b) Informed Traders and Uninformed Traders

Past theoretical studies suggested that trading volume is partiallydetermined by the interaction of informed and uninformedtraders. Unfortunately, previous studies fail to measure tradingactivity of informed and uninformed traders due to datalimitation. With a complete limit order book, we can constructproxies for informed trading and liquidity trading. We classifyinvestors as informed and uninformed traders based on the ordersize in terms of trading lots. Two lines of researches can rationalizethe use of order size to define informed and uninformed traders.Easley and O`Hara (1987) argued that informed traders tend totrade large amounts at any given price. The stealth tradinghypothesis proposed by Barclay and Warner (1993) hypothesizedthat informed traders tend to place medium to large orders.Recently, Lee, Lin and Liu (1999) provided evidence that bigindividual investors are the most well informed traders on theTaiwan Stock Exchange. Moreover, they found that small orders(uninformed orders) provide liquidity to the market.

In this study, orders with size greater than or equal to 20 lotsare defined as informed orders, and uninformed orders (orliquidity orders) are orders with less than 20 lots. The choice of20 lots as the cutting point is arbitrary. Nevertheless, 20 lotswould be regarded as a medium trade size in the TWSE. As thestealth trading hypothesis suggests, informed traders tend to split



their transaction into several medium trades. In addition, Lee,Lin and Liu (1999) also defined informed and uninformedtrades based on order size. They found that a cutting point of 10lots and 20 lots yielded similar empirical results.

4. EMPIRICAL RESULTS

(i) Trading Behaviors of Informed and Uninformed Traders

The distribution of buy and sell orders across the 31 timeintervals is shown in Table 1a. Orders are measured in terms oflots (LOTS) and the number of orders (NUM). The first session(OPEN) indicates the orders accumulated from 8:30 up to thefirst trade. The others are six-minute intervals. The times shownin the first column of Table 1a indicate when a six-minuteinterval is ended. For example, the second interval `9:06' standsfor the time period from 9:00 to 9:06 excluding the first trade.The last interval `12:00' stands for the interval from 11:54 to12:00. The time interval from 9:06±11:54 is defined as the interiorperiod, ìnn'. Regardless of the measurement unit, investors'orders display an unambiguous U-shape pattern. Total order isthe largest at the open, and the second largest order appears atthe market close. F-statistics indicate that total orders at the openand the close are significantly different from those in the interiorperiods (F-open,inn = 28.41; F-close,inn = 11.62). The findingsupports the first hypothesis, that is, investors tend to clustertheir orders at the market open and the close.

Trading lots and the number of orders at the open are almosttwo times of those at the market close. A detailed examination ofTable 1a indicates that this is mainly driven by the behavior of sellorders. Sell orders dominate buy orders at the market open. SellLOTS and NUM are 2719.49 and 257.36, respectively, comparedwith 1760.35 and 180.24 for the buy LOTS and NUM. There is arelatively small difference between buy orders at the open andthose at the close. Moreover, at the market close, the sizes of selland buy orders are similar. Buy LOTS and NUM are 1164.28 and105.25, respectively, compared with sell LOTS and NUM 1151.65and 102.99 respectively at the close. The large sell order at theopen could be a reflection of a high level of uncertainty.



Table 1a

Buy and Sell Orders

BUY SELL TOTAL(B+S)

LOTS NUM LOTS NUM LOTS NUM

Open 1760.35 180.24 2719.49 257.36 4479.84 437.609:06 602.81 52.99 694.13 57.18 1296.94 110.179:12 604.89 55.39 708.67 66.99 1313.56 122.389:18 560.28 52.60 587.43 57.00 1147.71 109.619:24 541.58 50.42 531.83 51.51 1073.41 101.949:30 484.80 46.36 513.14 49.97 997.93 96.339:36 473.05 43.99 508.72 48.78 981.77 92.779:42 445.80 43.01 461.68 45.40 907.48 88.419:48 391.69 39.38 429.67 42.29 821.36 81.679:54 380.62 37.69 412.28 41.04 792.90 78.73

10:00 364.86 36.34 409.96 40.41 774.82 76.7510:06 392.37 38.65 386.31 37.99 778.67 76.6410:12 387.12 38.31 393.43 38.74 780.54 77.0510:18 378.69 37.16 373.90 36.58 752.58 73.7410:24 351.54 35.10 348.03 34.46 699.57 69.5710:30 349.87 33.77 359.54 35.00 709.41 68.7710:36 329.60 33.08 350.43 33.42 680.04 66.5010:42 335.88 33.91 346.79 33.06 682.67 66.9710:48 348.37 35.59 324.18 31.47 672.54 67.0610:54 362.24 36.76 331.82 31.89 694.06 68.6511:00 347.51 35.35 339.35 32.82 686.86 68.1711:06 335.39 32.60 368.22 34.54 703.61 67.1411:12 353.05 34.68 376.94 35.67 729.99 70.3511:18 349.60 35.04 354.70 34.16 704.30 69.2111:24 359.91 36.53 327.70 32.82 687.60 69.3511:30 390.59 38.80 374.85 35.83 765.44 74.6311:36 428.38 41.02 423.74 40.02 852.12 81.0411:42 474.11 45.92 445.50 42.77 919.61 88.6911:48 582.22 55.89 510.43 48.90 1092.65 104.7911:54 662.32 66.31 600.93 58.05 1263.25 124.3612:00 1164.28 105.25 1151.65 102.99 2315.93 208.24AVERAGE 500.41 48.64 540.164 51.464 1024.49 98.62

F-all 9.96** 17.36** 20.28** 28.41** 15.38** 23.30**F-open, 9:06 13.19** 25.14** 25.66** 35.18** 20.47** 31.03**F-open, inn 18.92** 31.58** 34.77** 42.33** 28.41** 37.99**F-open, close 2.71 6.79* 12.94** 18.73** 7.62** 12.89**F-9:06, inn 2.11 1.47 4.10* 2.61 3.07 2.02F-9:06, close 6.04* 8.76** 3.72 7.88** 4.81* 8.40**F-close, inn 12.17** 14.65** 11.04** 16.51** 11.62** 15.64**

Notes:Orders are expressed in terms of lots (LOTS) and number of orders (NUM). One lotequals to 1,000 shares. F stands for F-statistic; àll' represents all trade intervals; òpen'represents the open trade; `9:06' represents the first six-minute interval (9:00±9:06)excluding the open trade; ìnn' represents interior periods from 9:06 to 12:00; `close'represents the last trade interval (11:54±12:00). *, ** indicates significance at the 1% and10% levels, respectively.



Table 1b

Order/Volume Ratio and Order Price Spread

Time Interval Order/Volume Ratio Order Price Spread

Open 27.7995 1.643989:06 4.7335 ÿ0.348129:12 4.5707 ÿ0.256779:18 4.2773 ÿ0.292719:24 4.2927 ÿ0.344819:30 3.5854 ÿ0.398529:36 3.5945 ÿ0.393539:42 3.9469 ÿ0.430509:48 3.3957 ÿ0.439479:54 3.3979 ÿ0.44465

10:00 3.4905 ÿ0.5187610:06 3.3758 ÿ0.5261910:12 3.3008 ÿ0.5454110:18 3.1984 ÿ0.5557010:24 3.3374 ÿ0.5873310:30 3.2276 ÿ0.6540310:36 3.2233 ÿ0.6271010:42 3.1608 ÿ0.6396810:48 3.0454 ÿ0.6492910:54 2.8778 ÿ0.6956811:00 2.7656 ÿ0.7115911:06 3.0240 ÿ0.7256011:12 2.8690 ÿ0.7483911:18 2.8316 ÿ0.8320411:24 2.6757 ÿ0.8470611:30 2.4353 ÿ0.9117511:36 3.0604 ÿ0.9552611:42 2.2635 ÿ1.0950211:48 1.9511 ÿ1.2455811:54 1.7914 ÿ1.4730112:00 1.3044 ÿ2.53481AVERAGE 3.9614 ÿ0.63821

F-all 65.72** 62.48**F-open, 9:06 77.50** 148.61**F-open, inn 95.04** 246.18**F-open, close 110.83** 289.81**F-9:06, inn 4.19* 8.58**F-9:06, close 22.09** 95.34**F-close, inn 75.98** 77.82**

Notes:Orders and volume in a certain trade session are expressed in terms of lots (LOTS). Onelot equals to 1,000 shares. Order price spread of a stock equals to average selling priceminus average buying price for the stock in a certain trade session. F stands for F-statistic;àll' represents all trade intervals; òpen' represents the open trade; `9:06' represents thefirst six-minute interval (9:00±9:06) excluding the open trade; ìnn' represents interiorperiods from 9:06 to 12:00; `close' represents the last trade interval (11:54±12:00).*, ** indicates significance at the 1% and 10% levels, respectively.



Interestingly, while the largest total order appears at the open,trading volume (as shown in Figure 1) is at its peak at the marketclose. Table 1b shows the order/volume ratio and the order pricespread (OPS). Order price spread of a stock equals averageselling price minus average buying price for the stock in a certaintrade session. The number shown in Table 1b is the average OPSof the 30 sample firms. The order/volume ratio is extremely highat the open and then decreases gradually. On the other hand,OPS is positive at the open but becomes negative afterwards. Ahigh order/volume ratio and a large OPS imply a low chance fororders to be executed and vice versa. Therefore, Table 1b furtherillustrates that many of the orders placed at the open are notexecutable. As investors may place orders conservatively, totalorder may not be a good measure of real trading intention.Therefore, it is important to distinguish real orders from waitingorders ± the orders which are less likely to be executed.

To examine why large open orders do not lead to large tradingvolume, we decompose total orders into real and waiting orders.This decomposition is important to identify the real tradingintention of investors. To `test' the market, investors may placeorders that are not likely to be executed. As defined earlier, realbuy (sell) orders are those that have quotes greater (lower) than orequal to two ticks from the previous transaction prices. Buy (sell)orders that have quotes lower (greater) than or equal to two ticksfrom the previous transaction prices are classified as waiting orders.

As shown in Table 2, the largest waiting orders occur at theopen. Only 38% [664.71/(664.71 + 1095.64)] of buy orders and31% [(839.11/(839.11 + 1880.38)] of sell orders at the open arereal orders. Waiting orders dramatically decrease after themarket open and become stable after one hour of trading. Thisis probably due to high uncertainty existing at the market open.As information releases gradually, investors are willing to placemore executable orders. Therefore, waiting orders decreasecontinuously since the open trade. Regardless of the fact thatwaiting orders increase slightly at the market close, real buy andreal sell orders are the largest at the market close. About 93%(1082.14/1164.28) of buy orders and 94% (1083.06/1151.65) ofsell orders are real orders. This implies that through trading,private information is revealed and traders are less conservative atthe close than at the open. To sum up, results from Table 2



Table 2

Real and Waiting Orders in Terms of Lots in Trades

Time Real Orders Waiting OrdersInterval

BUY SELL BUY SELL

Open 664.71 839.11 1095.64 1880.389:06 476.81 475.80 126.00 218.339:12 475.25 520.70 129.64 187.979:18 457.59 442.16 102.69 145.279:24 443.85 411.04 97.74 120.819:30 412.90 399.52 71.90 113.639:36 388.05 410.00 85.00 98.739:42 371.87 378.04 73.93 83.639:48 331.70 356.98 59.99 72.689:54 321.35 344.56 59.26 67.70

10:00 314.12 340.88 50.73 69.0610:06 322.68 320.91 69.78 65.4110:12 325.07 334.51 62.04 58.9210:18 323.26 318.26 55.43 55.6410:24 304.92 303.07 46.54 44.9510:30 305.78 312.88 44.09 46.6710:36 286.06 303.76 43.54 46.6810:42 292.19 304.13 43.69 42.6610:48 305.76 281.24 42.60 42.9410:54 322.99 288.16 39.26 43.6611:00 304.65 298.17 42.86 41.1811:06 297.80 325.87 37.59 42.3511:12 308.71 335.20 44.34 41.7311:18 302.72 319.56 46.88 35.1411:24 316.43 297.52 43.47 30.1811:30 346.37 339.01 44.22 35.8411:36 385.43 380.67 42.95 43.0711:42 424.75 403.33 48.71 42.1711:48 527.65 461.06 54.57 49.3711:54 606.63 555.20 55.68 45.7312:00 1082.14 1083.06 82.14 68.59AVERAGE 398.39 402.72 94.93 128.42

F-all 5.15** 5.40** 21.77** 40.25**F-open, 9:06 1.95 5.15* 20.66** 35.42**F-open, inn 6.36* 10.04** 23.89** 42.98**F-open, close 3.78 1.03 22.73** 42.76**F-9:06, inn 1.32 1.44 6.35* 15.45**F-9:06, close 8.77** 8.35** 2.19 14.05**F-close, inn 13.60** 12.62** 1.39 0.05

Notes:F stands for F-statistic; àll' represents all trade intervals; òpen' represents the open trade;`9:06' represents the first six-minute interval (9:00±9:06) excluding the open trade; ìnn'represents the interior periods from 9:06 to 11:54; `close' represents the last trade interval(11:54±12:00). *, ** indicates significance at the 1% and 10% levels, respectively.



Table 3

Informed and Uninformed Orders

Time Real Orders Waiting Orders Informed/Interval

BUY SELL BUY SELL Uninf.Informed Uninf. Informed Uninf. Informed Uninf. Informed Uninf.

Open 410.53 254.17 545.21 293.91 714.92 380.72 1291.89 588.49 1.959:06 317.76 159.05 330.31 145.50 86.78 39.22 156.24 62.09 2.209:12 311.99 163.25 341.53 179.17 89.08 40.56 122.31 65.66 1.939:18 301.83 155.76 287.22 154.94 65.14 37.55 95.87 49.40 1.899:24 293.29 150.55 267.68 143.34 64.12 33.62 79.37 41.44 1.919:30 270.57 142.33 258.38 141.13 44.15 27.75 74.54 39.09 1.859:36 255.19 132.86 269.56 140.43 57.27 27.73 64.11 34.62 1.939:42 243.77 128.10 244.79 133.25 46.67 27.25 53.24 30.39 1.849:48 213.68 118.02 231.72 125.27 35.95 24.04 46.57 26.12 1.809:54 207.41 113.95 221.85 122.73 37.02 22.25 42.58 25.12 1.79

10:00 202.89 111.23 220.26 120.64 30.29 20.45 44.38 24.69 1.8010:06 207.19 115.40 205.05 115.85 45.31 24.47 43.70 21.70 1.8110:12 208.87 116.20 215.99 118.52 40.44 21.60 37.95 20.97 1.8110:18 210.22 113.05 205.51 112.75 35.14 20.28 36.48 19.16 1.8410:24 195.79 109.21 194.72 108.36 29.88 16.66 28.33 16.62 1.7910:30 201.24 104.54 202.79 110.09 28.15 15.94 29.71 16.96 1.8710:36 184.86 101.21 197.74 106.01 27.68 15.86 31.67 15.01 1.8610:42 188.11 104.08 198.49 105.64 27.08 16.61 28.55 14.11 1.8410:48 194.38 111.38 180.62 100.62 25.60 17.00 28.98 13.96 1.7710:54 207.70 115.28 186.46 101.70 23.40 15.86 30.12 13.54 1.8211:00 192.76 111.89 192.19 105.98 27.50 15.36 27.11 14.07 1.7811:06 195.65 102.15 213.56 112.31 23.71 13.88 28.81 13.54 1.91

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11:12 200.04 108.67 218.15 117.05 28.12 16.22 28.34 13.40 1.8611:18 194.74 107.99 207.13 112.42 29.27 17.61 22.91 12.24 1.8111:24 201.94 114.49 189.56 107.96 27.09 16.39 18.51 11.67 1.7411:30 222.46 123.91 218.02 120.99 28.11 16.11 24.02 11.82 1.8111:36 254.19 131.25 246.91 133.76 26.42 16.52 29.40 13.67 1.8911:42 279.52 145.88 259.22 144.11 29.94 18.77 28.22 13.95 1.8511:48 346.69 180.97 295.71 165.35 33.94 20.63 33.75 15.62 1.8611:54 389.84 216.80 354.22 200.98 34.61 21.07 29.51 16.22 1.7812:00 727.71 354.44 704.48 378.58 53.94 28.20 45.82 22.76 1.95AVERAGE 259.12 139.29 261.45 141.27 61.18 33.75 86.55 41.87 1.85

F-all 4.48** 6.11** 4.23** 7.54** 16.29** 35.39** 33.40** 49.30** 0.72F-open, 9:06 0.95 4.57* 3.21 8.27** 14.99** 36.05* 28.79** 45.24** 0.29F-open, inn 15.24** 41.64** 43.89** 70.63** 468.87** 1019.74** 961.91** 1390.40** 0.54F-open, close 4.22* 2.30 0.77 1.47 16.72** 38.62** 35.25** 52.83** 0.89F-9:06, inn 3.50 2.89 4.83* 1.01 26.59** 18.22** 97.89** 65.23** 2.68F-9:06, close 7.60** 10.80** 5.54* 17.64** 2.19 1.88 12.64** 16.02** 3.50*F-close, inn 98.07** 116.83** 85.60** 154.93** 3.28 2.76 0.11 0.01 0.15

Notes:Informed orders = orders with size 20 lots; F stands for F-statistic; àll' represents all trade intervals; òpen' represents the open trade; `9:06' representsthe first six-minute interval (9:00±9:06) excluding the open trade; ìnn' represents the interior periods from 9:06 to 11:54; `close' represents the lasttrade interval (11:54±12:00). *, ** indicates significance at the 1% and 10% levels, respectively.

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support our third hypothesis, that is, traders tend to placeconservative orders at the market open.

Another possible reason for large waiting orders at the open isrelated to the trading mechanism in the Taiwan stock market.The TWSE adopts an order-driven computerized trading systemallowing only limit orders. There are no specialists and a 7%price limit at the open and intraday price limit in the innertrading periods and the close are imposed. As a result, investorsmay tend to place more conservative orders at the open. F-statistics indicate that real buy and real sell orders exhibit a J-curve pattern, which is consistent with the behavior of tradingvolume listed in Figure 1. In particular, F-open, inn for real buy andsell orders are 6.36 and 10.04 respectively; and F-close, inn for buyand sell orders are 13.60 and 12.62, respectively. On the contrary,waiting orders exhibit a reverse J-shaped pattern. Results in Table2 indicate that while the largest buy order appears at the marketopen, real trading intentions are the strongest at the close. Thehuge number of real orders at the market close is consistent withboth the portfolio- rebalance need and risk-sharing motive.

Table 3 shows that informed and uninformed traders adoptsimilar strategy, that is, they place large conservative orders at themarket open. By definition, informed trader's order is largerthan uninformed traders, we cannot make judgement on therelative importance of informed orders and uninformed ordersin explaining the J-shaped pattern of trading volume merelybased on order size. The last column of Table 3 shows the ratio ofinformed to uninformed orders. The ratio allows us to examine ifthe relative trading behavior between informed and uninformedinvestors changes overtime. The range of the ratio is (1.77±2.20).According to the F-statistics, the ratio is not significantly differentacross different sessions. The only exception is that the ratio forthe period `9:00±9:06' is significantly higher than that at theclosing period. This indicates that the relative trading behaviorbetween informed and uninformed investors is quite stable overtime. In addition, it is of interest to see if a particular type oforder is more likely to be executed at the open. We find thatwhile the informed orders counts 62% of the total orders, only41% of the executed orders are informed orders. This means thatuninformed orders account for 59% of the executed orders eventhough they account for only 38% of the total orders. In



addition, 7.6% of informed orders and 10.7% of uninformedorders are executed at the open; the difference is statisticallysignificant. These figures imply that small orders are more likelyto be executed at the open. An examination of the pricingbehavior indicates that small orders tend to offer a better pricethan large orders. This explains the relatively high execution ratefor small orders at the market open.

(ii) Regression Analysis

To investigate the role of information trade and liquidity trade inexplaining intraday pattern of trading volume, we conduct thefollowing regression analysis and report the results in Table 4:

VOLt � a0 � a1 INFBt � a2 INFSt � a3 UNFBt � a4 UNFSt � "t

�1�where VOLt is the trading volume at time interval t; INFBt andINFSt are the buy and sell orders placed by informed traders,while UNFBt and UNFSt are buy and sell orders of uninformedtraders. For each of the trading intervals, the above regression isestimated for each of the 30 sample firms, respectively. Reportedcoefficient is the average of the coefficients for the 30 firms. Thet-statistics are calculated by using the coefficients obtained fromthe regression for each of the sample firms.

Table 4 shows that trading orders placed by both informed anduninformed traders are significantly related with trading volumein all time intervals. However, the explanatory power is the lowestat the market open and is the highest at the market close(adjusted R2 are 0.495 and 0.870 for the market open and marketclose respectively). This implies that most orders at market openare non-executable. In terms of the value of estimatedcoefficients, the impact of uninformed orders is greater thanthat of informed orders. In particular, coefficients of UNFB aregreater than that of INFB for all time intervals except at 11:48±11:54. For the sell orders, coefficients of UNFS are uniformlygreater than that of INFS except at 10:12±10:18. While bothinformation trading and liquidity trading can explain intradaytrading volume, the impact of liquidity trading is relatively larger.This is consistent with the study by Lee, Lin and Liu (1999) whichfind that small investors provide liquidity to the market.



Table 4

Volume Regression±Total Orders

VOLt � a0 � a1 INFBt � a2 INFSt � a3 UNFBt � a4 UNFSt � "t

Interval INTERCEP INFB INFS UNFB UNFS Adjusted R2

Open ÿ177.067** 0.14024** 0.06796** 0.20174** 0.14793** 0.49519:06 ÿ112.328** 0.40618** 0.23558** 1.09961** 0.78831** 0.78959:12 ÿ106.836** 0.37540** 0.22833** 0.95579** 0.66170** 0.76519:18 ÿ82.129** 0.42648** 0.23936** 1.16678** 0.48987** 0.79819:24 ÿ62.670** 0.48336** 0.22434** 1.01721** 0.54073** 0.77509:30 ÿ73.169** 0.47751** 0.26483** 0.90254** 0.75828** 0.74479:36 ÿ69.443** 0.38743** 0.38401** 0.98304** 0.68453** 0.76089:42 ÿ40.543** 0.41405** 0.39811** 0.84710** 0.64315** 0.75249:48 ÿ37.101** 0.52033** 0.40850** 0.60507** 0.80262** 0.75739:54 ÿ42.590** 0.46304** 0.41332** 1.01925** 0.54311** 0.7361

10:00 ÿ50.423** 0.49785** 0.32878** 0.93595** 0.77886** 0.759010:06 ÿ12.428 0.37317** 0.42435** 1.14129** 0.42536** 0.778010:12 ÿ32.656** 0.46900** 0.41323** 0.75377** 0.59591** 0.753710:18 ÿ18.047 0.43212** 0.45091** 1.02498** 0.36773** 0.763310:24 ÿ18.165 0.49888** 0.38710** 0.86846** 0.58085** 0.6993

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10:30 ÿ49.270** 0.46198** 0.39674** 0.96970** 0.84006** 0.761210:36 ÿ63.287** 0.51419** 0.42454** 0.99913** 0.87841** 0.762110:42 ÿ44.263** 0.51057** 0.45332** 0.79265** 0.82062** 0.757710:48 ÿ49.344* 0.48839** 0.44256** 1.06329** 0.82993** 0.777810:54 ÿ37.729** 0.62150** 0.26850** 0.80926** 1.08982** 0.784911:00 ÿ29.904* 0.48771** 0.40427** 0.83176** 0.83262** 0.774011:06 ÿ102.849* 0.37685** 0.58740** 0.95854** 1.06801** 0.795011:12 ÿ30.498** 0.40893** 0.52204** 0.76776** 0.96060** 0.783811:18 1.043 0.49421** 0.42080** 0.70429** 0.76029** 0.770011:24 ÿ15.131 0.40428** 0.37986** 0.94874** 0.81792** 0.739611:30 ÿ30.893* 0.54376** 0.55733** 0.82389** 0.71485** 0.797911:36 ÿ49.558** 0.50999** 0.47605** 0.99202** 1.00933** 0.793611:42 ÿ33.809 0.52192** 0.56749** 0.67404** 0.92178** 0.787111:48 0.374 0.63447** 0.49797** 0.79482** 0.87166** 0.808611:54 ÿ19.527 0.64526** 0.52474** 0.59342** 1.04824** 0.853512:00 92.139* 0.59978** 0.66907** 0.92636** 1.15506** 0.8695

Notes:VOLt is the trading volume at time interval t; INFBt and INFSt are the buy and sell orders placed by informed traders, while UNFBt and UNFSt are buyand sell orders of uninformed traders. For each of the trading intervals, the above regression is estimated for each of the 30 sample firms, respectively.Reported coefficient is the average of the coefficients for the 30 firms. The T-statistics are calculated by using the coefficients obtained from theregression for each of the sample firms. *, ** indicates significance at the 1% and 10% levels, respectively.

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Table 5

Volume Regressions±Real/Waiting Orders

VOLt � a0 � a1 INFBRt � a2 INFSRt � a3 UNFBRt � a4 UNFSRt � a5 INFBWt � a6 INFSWt�a7 UNFBWt � a8 UNFSWt � "t

Interval INTERCEP INFBR INFSR UNFBR UNFSR INFBW INFSW UNFBW UNFSW Adjusted R2

Open ÿ155.664** 0.29827** 0.22743** 0.57864** 0.52367** 0.01383 0.01743 0.07496 0.03672 0.65029:06 ÿ57.263** 0.46459** 0.33230** 1.27739** 1.15697** 0.01530 0.04144 ÿ0.29319 ÿ0.08964 0.83279:12 ÿ69.310** 0.47046** 0.30600** 1.17194** 0.92263** 0.05222 ÿ0.02205 ÿ0.05269 ÿ0.06593 0.80409:18 ÿ66.464** 0.47786** 0.34084** 1.18693** 0.67539** 0.18133 0.15628 0.67285* ÿ0.15800 0.83069:24 ÿ47.357** 0.55347** 0.34415** 1.13025** 0.73423** 0.09041 ÿ0.06501 0.28633 ÿ0.38708 0.80849:30 ÿ62.382** 0.55127** 0.37954** 1.07483** 1.08425** ÿ0.05829 ÿ0.12886 ÿ0.10922 ÿ0.63562 0.78289:36 ÿ58.385** 0.41273** 0.48126** 1.19042** 1.04315** 0.25194 0.00040 ÿ0.33523 ÿ1.04445** 0.80339:42 ÿ34.847** 0.49309** 0.39765** 1.15042** 0.79481** 0.08256 0.10776 ÿ0.08331 ÿ0.20133 0.78449:48 ÿ43.789** 0.55435** 0.50039** 0.92682** 0.94770** 0.01136 ÿ0.20452* ÿ0.36881 ÿ0.25390 0.78399:54 ÿ41.114** 0.49486** 0.45474** 1.21168** 0.75340** 0.01812 0.03731 0.86169* ÿ0.21204 0.7809

10:00 ÿ55.563** 0.50462** 0.47818** 1.05875** 0.91820** ÿ0.03817 ÿ0.21786 0.17601 0.00599 0.796210:06 ÿ47.207** 0.48700** 0.51142** 1.33100** 0.66480** 0.03155 0.15123 0.51694 ÿ1.09909** 0.823310:12 ÿ41.729** 0.51513** 0.47417** 0.81963** 0.90082** 0.14991 0.13887 0.32271 ÿ0.77314* 0.788310:18 ÿ39.743* 0.47025** 0.49287** 1.20416** 0.66784** 0.15341 ÿ0.04723 0.03471 ÿ0.58885 0.7963

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10:24 ÿ20.057 0.55099** 0.48846** 0.98342** 0.82527** 0.08318 ÿ0.34249* ÿ0.00261 ÿ0.58373 0.734410:30 ÿ47.761** 0.48630** 0.45592** 1.07349** 0.93003** 0.09920 0.20703 ÿ0.03461 ÿ0.03698 0.785010:36 ÿ62.282** 0.54169** 0.46699** 1.15224** 1.11969** 0.34842 0.03618 0.31207 ÿ0.86429* 0.801010:42 ÿ49.119** 0.53659** 0.56996** 0.98865** 1.09666** 0.01366 ÿ0.04675 ÿ0.45243 ÿ1.44598* 0.797410:48 ÿ63.342** 0.52269** 0.53338** 1.19527** 1.29695** ÿ0.22299 ÿ0.35624 ÿ0.55371 ÿ0.72369 0.820310:54 ÿ46.776** 0.64485** 0.33462** 0.99797** 1.29082** ÿ0.04600 0.16212 ÿ0.33889 ÿ0.45035 0.807811:00 ÿ54.993** 0.51107** 0.53219** 1.02295** 1.07469** ÿ0.04818 ÿ0.23189 0.05457 ÿ0.65205 0.809711:06 ÿ97.988** 0.41097** 0.58819** 1.12968** 1.27574** ÿ0.02716 0.67099* 0.24515 ÿ0.54198 0.825711:12 ÿ42.948** 0.48394** 0.55487** 0.82399** 1.15251** ÿ0.07129 0.22644 0.67905 ÿ0.51464 0.808211:18 ÿ22.069 0.56440** 0.47232** 1.05275** 0.97542** ÿ0.17194 ÿ0.09458 ÿ0.20426 ÿ2.19387** 0.805711:24 ÿ19.981 0.40480** 0.42062** 1.13585** 1.02623** 0.46152 0.41422 ÿ0.11928 ÿ1.22901* 0.771411:30 ÿ50.916** 0.60032** 0.55558** 0.98584** 1.06398** ÿ0.12989 0.50911* 0.00331 ÿ1.52084* 0.823111:36 ÿ57.117** 0.52569** 0.51095** 1.07626** 1.28282** ÿ0.04392 0.26476 ÿ0.54067 ÿ1.25610* 0.818811:42 ÿ70.041** 0.55269** 0.63671** 0.88167** 1.12054** 0.24280 ÿ0.57630 1.42087 ÿ0.26787 0.828811:48 ÿ42.284** 0.70395** 0.58829** 0.91922** 1.08144** 0.03681 ÿ0.10590 0.25191 ÿ0.22031 0.833811:52 ÿ35.678* 0.67791** 0.51020** 0.66898** 1.18555** 0.43140 0.59116 0.27954 ÿ1.18646 0.878012:00 48.457 0.62460** 0.70482** 1.01444** 1.25638** 0.41026 0.47690 ÿ0.48833 ÿ1.14357 0.8876

Notes:VOLt is the trading volume at time interval t. For the independent variables, INF and UNF stand for informed and uninformed traders, respectively.The fourth character identifies whether it is a sell (S) or a buy (B) order. The final character indicates whether it is a real (R) or a waiting (W) order.For each of the trading intervals, the above regression is estimated for each of the 30 sample firms, respectively. Reported coefficient is the average ofthe coefficients for the 30 firms. The T-statistics are calculated by using the coefficients obtained from the regression for each of the sample firms.*, ** indicates significance at the 1% and 10% levels, respectively.

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To investigate the impact of real and waiting orders, wedecompose orders into real and waiting orders. Hence, tradingvolume is regressed on the real and waiting orders placed byinformed and uninformed traders, i.e.:

VOLt � a0 � a1 INFBRt � a2 INFSRt � a3 UNFBRt

� a4 UNFSRt � a5 INFBWt � a6 INFSWt

� a7 UNFBWt � a8 UNFSWt � "t : �2�For the independent variables, INF and UNF stand for informedand uninformed traders, respectively. The fourth characteridentifies whether it is a sell (S) or a buy (B) order. The finalcharacter indicates whether it is a real (R) or a waiting (W) order.

As shown in Table 5, waiting order has insignificant impacts ontrading volume in most of the cases. In particular, none of thecoefficients of INFBW is significant. Therefore, the J-shapepattern of trading volume is mainly driven by real orders.Compared with Table 4, the coefficients of real orders (INFBR,INFSR, UNFBR and UNFSR) are higher. Therefore, waitingorders under-estimate the relationship between trading intentionand intraday pattern of trading volume. While the significantlevel of informed and uninformed orders are the same,coefficients of uninformed orders (UNFBR and UNFSR) arehigher than those of informed orders (INFBR and INFSR) in allinstances. This further supports the findings in Table 4; liquiditytrading plays a more important role than informed trading inexplaining the intraday pattern of trading volume.

(iii) Robustness of the Results

To test the robustness of the above results, we replicate the aboveanalyses using alternative definitions of real/waiting orders anduninformed/informed orders as stated in Table 6. Moreover, weattempt different regression specifications explained below. Thedistributions of different types of orders based on variousclassification criteria are presented in Figure 2. Figure 2illustrates that intraday patterns of different types of orders areparallel to those reported in Table 3. In particular, real orders ofboth informed and uninformed investors follow a `J' shapedpattern. On the contrary, waiting orders are extremely high at the



open but decrease dramatically afterwards. A slight difference isthat for cases (3), (4), (5), and (6), real orders at the open areless than those in the period `9:00±9:06'. To sum up, thedistributions of orders reported in Tables 2 and 3 are invariant toclassification criteria.

For the regression analysis, we attempt the followingalternatives: (a) pooling all firms in each interval and estimatingequations (1) and (2); (b) pooling all firms across all timeintervals and adding 31 time dummies; trading volume is thenregressed on the dummies and the interaction terms of dummiesand the independent variables as stated in equations (1) and (2);(c), same as (a) but including stock returns as control variables;and (d) same as (b) but including stock returns as controlvariables. Table 7 summarizes the key results for alternativeregression specifications and order classification schemes. PanelA of Table 7 indicates that the main results drawn from Table 4hold. In particular, uninformed orders have larger coefficientsthan the informed orders. When pooling data is used, a majordifferent result is found in Panel B ± most of the coefficients ofwaiting orders are significantly different from zero. However, thecoefficients of real orders are still uniformly greater than those ofwaiting orders. The coefficients of uninformed orders are greaterthan those of informed orders for real orders, but the reverserelationship is found for waiting orders for cases 3 and 5. Exceptslight variations, the main conclusions inferred from Tables 4and 5 still hold. Our earlier results are robust with respect to theorder classification scheme and regression specifications.

5. CONCLUSIONS

Previous theoretical researches suggested that trading volumesdepend on traders' exogenous liquidity needs, information flows,and the strategic interactions between informed and liquiditytraders. Constrained by order flow data unavailability, previousstudies examine indirectly concentrated trading using tradingvolume data. The pivotal contribution of this study is to measurethe intraday trading behavior of informed and uninformedinvestors directly using a complete limit order book data of theTaiwan Stock Exchange. We examine the intraday pattern of



Figure 2

Distributions of Orders for Different Classification Schemes

Case 1Real Orders

Case 2Real Orders

Case 3Real Orders



Figure 2 (Continued)

Case 1Waiting Orders






Case 4Real Orders

Case 5Real Orders

Case 6Real Orders







Note:Case 1±Case 6 are referred to different classification schemes as defined in Table 6.



Table 6

Alternative Specifications of Informed/Uninformed and Real/Waiting Orders

Order Type Case 1 (the Case 2 Case 3 Case 4 Case 5 Case 6base case)*

Informed orders orders orders orders orders ordersorders > 20 lots > 20 lots > 20 lots > 20 lots > 20 lots > 20 lotsUninformed orders orders orders orders orders ordersorders �20 lots < 5 lots �20 lots < 5 lots �20 lots < 5 lotsReal buy order price order price order price order price order price order priceorders ptp ÿ 2 ticks ptp ÿ 2 ticks �ptp �ptp �ptp + 2 ticks ptp + 2 ticksReal sell order price order price order price order price order price order priceorders �ptp + 2 ticks �ptp + 2 ticks �ptp �ptp �ptp ÿ 2 ticks �ptp ÿ 2 ticksWaiting buy order price order price order price order price order price order priceorders < ptp ÿ 2 ticks < ptp ÿ 2 ticks < ptp < ptp < ptp + 2 ticks < ptp + 2 ticksWaiting sell order price order price order price order price order price order priceorders > ptp + 2 ticks > ptp + 2 ticks > ptp > ptp > ptp ÿ 2 ticks >ptp ÿ 2 ticks

Notes:* ptp = price of the previous transaction.Case 1 is the base case. Results reported in Table 1±Table 5 of the text are based on the definitions stated in Case 1.

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Table 7

Comparison of Regression Results Among Different Specifications and Definitions of Real/Waiting Orders andInformed/Uninformed Orders

Panel A

Major Findings in Table 4 Case 1 Case 2 Case 3 Case 4 Case 5 Case 6

a b c d a b c d a b c d a b c d a b c d a b c d

(1) Coefficients of informed orders ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓and uninformed orders aresignificant in all time intervals

(2) Coefficients of uninformed ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓orders > coefficients ofinformed orders

Panel B

Major Findings in Table 5 Case 1 Case 2 Case 3 Case 4 Case 5 Case 6

a b c d a b c d a b c d a b c d a b c d a b c d

(1) Coefficients of real orders are ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓significantCoefficients of waiting orders ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗are insignificant

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Table 7 (Continued)

(2) Coefficients of real orders >Coefficients of waiting orders± for informed orders ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓± for uninformed orders ✓ ✓ ✓ ✓ ? ? ? ? ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

(3) Coefficients of uninformedorders >Coefficients of informed order± for real orders ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓± for waiting orders ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✗ ✗ ✗ ✓ ? ✓ ✓ ✗ ✗ ✗ ✗ ? ? ? ?

Notes:Case 1 to Case 6 are different from each other in the ways to classify orders. The definitions of real/waiting and informed/uninformed orders for eachcase are shown in Table 6. (a)±(d) represent different regression specifications. (a) For each time interval, data for all the sample firms are pooled,equations (1) and (2) are then estimated for each time interval; (b) Data for all the sample firms across all time intervals are pooled. Dummies for the31 time intervals are added, trade volume are then regressed on the interval dummies and the interaction term between dummies and theindependent variables stated in equation (1) and (2); (c) Same as (a), adds stock returns as control variables; (d) Same as (b), adds stock returns ascontrol variables.`✓': more than 2/3 of the coefficients are consistent with the result listed in the first column; `✗: more than 2/3 of the coefficients are opposite to theresult listed in the first column; `?': less than 2/3 but more than 1/3 of the coefficients are consistent with the result listed in the first column.

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information and liquidity orders as well as the ordering strategiesof both informed and uninformed (liquidity) traders.

The results of this study indicate that investors have strongdesires to place orders at the market open and the close. While thelargest orders are placed at the open, only mediocre tradingvolume is observed. This implies that traders tend to placeconservative orders at the open. To take into account the strategicinteraction of informed and liquidity traders, we classify totalorders into real orders and waiting orders. Such a classificationallows us to distinguish real trading intention from desire for pricepriority. Our findings show that real orders from both informedand uninformed traders exhibit a J-shaped intraday pattern, whichis consistent with the intraday pattern of trading volume. On theother hand, a reverse J-shaped pattern of waiting orders is found asorders at the market open are less likely to be executed. Investorstend to `test' the market when uncertainty at the market open ishigh. However, as trading is taking place, information is releasedand uncertainty is gradually resolved. As a consequence, theamount of waiting orders is significantly reduced.

Results from regression analysis indicate that both informationand liquidity trading play an important role in explaining theintraday pattern of trading volume. We find that the impact ofliquidity trade on trading volume is slightly greater than that ofinformation trade. The possible reason is that uninformed ordersprovide liquidity to the market. Finally, waiting orders play a lesssignificant role than real orders in determining the intradaypattern of trading volume. This pinpoints the importance ofdistinguishing real trading intention from desires for pricepriority in studying the regularities of trading volume.

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Explaining Intraday Pattern of Trading Volume From the Order Flow Data

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